Erasures typically refer to error codewords in a symbol (e.g., 2D symbol, 2D machine-readable symbol, 2D barcode, 1D barcode, data matrix, etc.). Therefore, an identified erasure is typically characterized as having incorrect patterns at known locations. In some instances, when a symbol is partially out of the field of view of a symbol reader, and/or has pixel images where most if not all of the characters are black or white, erasure correction can be applied to the error codewords to facilitate in the decoding of encoded data in symbols. In particular, the scanning performance of a symbol reader (e.g., 2D machine-readable symbol reader, machine-readable symbol reader, laser scanner, bar code reader, etc.) can be improved, in the event that the symbol is damaged, is partially outside of the field of view of the machine-readable symbol reader, and/or otherwise partially un-readable.
In some instances, error codewords are detected in symbols where a substantial portion of the pixels are black or white. In particular, for QR codes (Quick Response codes), the errors can typically be detected in encoded codewords as mostly, if not all, black or white. However, after codewords have been unmasked (e.g., by one of the eight masks shown in FIG. 7), the error codewords in a QR code that were originally identifiable via detecting substantial black portions or substantial white portions are now unmasked. The white/black cells that were encoded now have a pattern (e.g., value/sequence) that is dependent on, for example, the mask applied, the mask value at the particular location of the white/black portions, and the codeword locations.